Global navigation satellite system(GNSS)carrier phase observations are two orders of higher accuracy than pseudo-range observations,and they are less affected by multipath *** a result,the time transfer accuracy can r...
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Global navigation satellite system(GNSS)carrier phase observations are two orders of higher accuracy than pseudo-range observations,and they are less affected by multipath *** a result,the time transfer accuracy can reach 0.1 ns,and the frequency transfer stability can reach 1×10^-15 with carrier phase(CP)method,therefore CP method is considered the most accurate and promising time transfer *** focus of this paper is to present a comprehensive summary of CP method,with specific attention directed toward day-boundary clock jump,ambiguity resolution(AR),multi-system time transfer and real-time time ***-boundary clock jump is essentially caused by pseudo-range *** approaches were proposed to solve the problem,such as continuously processing strategy,sliding batch and bidirectional filtering methods which were compared in this ***,researches on AR in CP method were *** scholars attempted to fix the single-difference ambiguities to improve the time transfer result,however,owing to the uncalibrated phase delay(UPD)was not considered,the current studies on AR in CP method were still ***,because four GNSS systems could be used for time-transfer currently,which was helpful to increase the accuracy and reliability,the researches on multi-system time transfer were ***’s more,real-time time transfer attracted more attention nowadays,the preliminary research results were presented.
To tackle the problem of aquatic environment pollution,a vision-based autonomous underwater garbage cleaning robot has been developed in our *** propose a garbage detection method based on a modified YOLOv4,allowing h...
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To tackle the problem of aquatic environment pollution,a vision-based autonomous underwater garbage cleaning robot has been developed in our *** propose a garbage detection method based on a modified YOLOv4,allowing high-speed and high-precision object ***,the YOLOv4 algorithm is chosen as a basic neural network framework to perform object *** the purpose of further improvement on the detection accuracy,YOLOv4 is transformed into a four-scale detection *** improve the detection speed,model pruning is applied to the new *** virtue of the improved detection methods,the robot can collect garbage *** detection speed is up to 66.67 frames/s with a mean average precision(mAP)of 95.099%,and experimental results demonstrate that both the detection speed and the accuracy of the improved YOLOv4 are excellent.
The technological process of the churning process in continuous butter manufacture were considered. The qualitative indicator of the water content of butter was modeled on the basis of a set of industrial data using a...
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ISBN:
(纸本)9781665492768
The technological process of the churning process in continuous butter manufacture were considered. The qualitative indicator of the water content of butter was modeled on the basis of a set of industrial data using an artificial neural network (ANN). The prototype of an intelligent system for predicting of the water content of butter allows to increase the information support of the operator-technologist. The proposed model predicts the water content of butter with an error of less than 2%. The input variables of the forecast model prototype are cream fat content, cream ripening temperature, cream supply temperature, frequency of revolutions of the stirrer of the whipping device, and consumption of the normalizing component. Further research is aimed at the development of a decision support information system for dairy manufacture and should be integrated into the subsystem of automated control of the technological process to ensure proper functioning in real time.
The problems associated with the operation of overhead power lines and ways of improving control over their condition with the help of UAVs are considered. A structural diagram of the system of technical diagnostics o...
The problems associated with the operation of overhead power lines and ways of improving control over their condition with the help of UAVs are considered. A structural diagram of the system of technical diagnostics of overhead lines based on UAVs was developed, for which the necessary diagnostic parameters were selected according to informative criteria. An analysis of types of UAVs was carried out in order to determine their suitability and efficiency of use for diagnosing the condition of overhead lines.
Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field a...
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In the field of electric power distribution network operation, most tasks involve contact operations. A key technology to enable the flexible operation of robots in live distribution network tasks is the installation ...
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This paper designs a data-driven controller for unknown linear systems whose actuators suffer from false data injection (FDI) attacks, using only noisy input-state data. To achieve this objective, a general FDI attack...
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ISBN:
(数字)9798350354409
ISBN:
(纸本)9798350354416
This paper designs a data-driven controller for unknown linear systems whose actuators suffer from false data injection (FDI) attacks, using only noisy input-state data. To achieve this objective, a general FDI attack model is introduced, which imposes constraints only on the switching frequency of attack channels and the magnitude of attack matrices. A time-varying state feedback control law is designed based on offline and online input-state data, which adapts to the channel switching of FDI attacks. This is achieved by solving a data-based semi-definite programs (SDPs) on-the-fly such that stabilizing the set of subsystems consistent with both offline clean data and online attack-corrupted data. It is shown that under mild conditions on the attack and the noise, the feasibility of the proposed SDP guarantees that the controller stabilizes the attack-corrupted system. A numerical example is presented to validate the effectiveness of the proposed method.
For on-policy reinforcement learning, discretizing action space for continuous control can easily express multiple modes and is straightforward to optimize. However, without considering the inherent ordering between t...
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Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. The higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accura...
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Coke pushing current is an indicator to evaluate the difficulty of coke pushing operation. The higher coke pushing current is, the greater coke pushing resistance is, and the more difficult coke is to push out. Accurate prediction of current peak during future coke pushing operation can provide more time for production personnel to adjust production status and avoid difficult coke pushing in carbonization chamber. In this paper, a combination prediction model based on VMD (Variational Mode Decomposition) and improved ARIMA (Autoregressive Integrated Moving Average) models is proposed. Firstly, VMD algorithm is used to decompose time series of coke pushing current peak, de-noising data, and extracting main information of time series. Then, ARIMA model is used to predict mean change of linear elasticity and GARCH (Generalized Autore-gressive Conditional Heteroskedasticity) model is introduced to predict ARIMA model residual and improve heteroscedasticity of nonlinear part of time series, and then ARIMA-GARCH model is established. Finally, predicted value is obtained by the sum of each component prediction. The experimental results show that the proposed prediction model has a high prediction accuracy in the short-term prediction of coke pushing current peak. The scheme is applied to actual coking production to guide production of coke.
Based on fractional calculus theory and reaction-diffusion equation theory, a fractional-order time-delay reaction-diffusion neural network with Neumann boundary conditions is investigated. By constructing the phase s...
Based on fractional calculus theory and reaction-diffusion equation theory, a fractional-order time-delay reaction-diffusion neural network with Neumann boundary conditions is investigated. By constructing the phase space basis based on the Laplace operator eigenvector, the system equation is linearized to obtain the characteristic equation. Then, the characteristic equation is analyzed, and the local stability of the system at the equilibrium point is discussed. And taking the time delay as the bifurcation parameter, the stability changes of the system at the equilibrium point and the generation conditions of the Hopf bifurcation are studied when the time delay changes. Moreover, a state feedback controller is designed to control the bifurcation of the system. Finally, the theoretical derivation is verified by numerical simulation.
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